QoS-adaptive approximate real-time computation for mobility-aware IoT lifetime optimization

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Department of Electrical and Computer Engineering


In recent years, the Internet of Things (IoT) has promoted many battery-powered emerging applications, such as smart home, environmental monitoring, and human healthcare monitoring, where energy management is of particular importance. Meanwhile, there is an accelerated tendency toward mobility of IoT devices, either being transported by humans or being mobile by itself. Existing energy management mechanisms for battery-powered IoT fail to consider the two significant characteristics of IoT: 1) the approximate real-time computation and 2) the mobility of IoT devices, resulting in unnecessary energy waste and network lifetime decay. In this paper, we explore mobility-aware network lifetime maximization for battery-powered IoT applications that perform approximate real-time computation under the quality-of-service (QoS) constraint. The proposed scheme is composed of offline and online stages. At offline stage, an optimal mobility-aware task schedule that maximizes network lifetime is derived by using mixed-integer linear programming technique. Redundant executions due to mobility-incurred overlapping of a single task on different IoT devices are avoided for energy savings. At online stage, a performance-guaranteed and time-efficient QoS-adaptive heuristic based on cross-entropy method is developed to adapt task execution to the fluctuating QoS requirements. Extensive simulations based on synthetic applications and real-life benchmarks have been implemented to validate the effectiveness of our proposed scheme. Experimental results demonstrate that the proposed technique can achieve up to 169.52% network lifetime improvement compared to benchmarking solutions.

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems